Performance among different types of myocontrolled tasks is not related

Heerschop, Anniek; Sluis, Corry K. van der; Otten, Egbert; Bongers, Raoul M.

doi:10.1016/j.humov.2020.102592

Cited by 11 publications

(14 citation statements)

References 45 publications

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“…There were significant correlations between the time taken to decide which muscle to activate to operate the hand and a measure of gaze behaviour, between one measure of tracking an EMG signal and the time taken to complete the cylinder task, and between another EMG tracking measure and one of the measures relating to symmetry of real-world arm use, however, these correlations were fairly weak (τ b = 0.332 to 0.357). Researchers have shown that performance on a given myoelectric task does not correlate with performance on a different myoelectric task, suggesting that a general myocontrol skill does not exist 13 . Another study from the same group found no relationship between skill in controlling EMG and performance on a functional task 10 .…”

Section: Discussionmentioning

confidence: 99%

Addressing unpredictability may be the key to improving performance with current clinically prescribed myoelectric prostheses

Chadwell

Kenney

Thies

et al. 2021

Sci Rep

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The efferent control chain for an upper-limb myoelectric prosthesis can be separated into 3 key areas: signal generation, signal acquisition, and device response. Data were collected from twenty trans-radial myoelectric prosthesis users using their own clinically prescribed devices, to establish the relative impact of these potential control factors on user performance (user functionality and everyday prosthesis usage). By identifying the key factor(s), we can guide future developments to ensure clinical impact. Skill in generating muscle signals was assessed via reaction times and signal tracking. To assess the predictability of signal acquisition, we inspected reaction time spread and undesired hand activations. As a measure of device response, we recorded the electromechanical delay between electrode stimulation and the onset of hand movement. Results suggest abstract measures of skill in controlling muscle signals are poorly correlated with performance. Undesired activations of the hand or incorrect responses were correlated with almost all kinematics and gaze measures suggesting unpredictability is a key factor. Significant correlations were also found between several measures of performance and the electromechanical delay; however, unexpectedly, longer electromechanical delays correlated with better performance. Future research should focus on exploring causes of unpredictability, their relative impacts on performance and interventions to address this.

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Section: Discussionmentioning

confidence: 99%

Addressing unpredictability may be the key to improving performance with current clinically prescribed myoelectric prostheses

Chadwell

Kenney

Thies

et al. 2021

Sci Rep

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“…Since it is probable that training will always be comparatively lengthy, we are developing gamified protocols to target user engagement. A corresponding technical challenge will be to validate that learned skills actually transfer to prosthesis control [48]. We do not view the work presented as controller implementations per se, rather as a framework within which arbitrary multidimensional spaces can be adapted according to the availability of control channels.…”

Section: Discussionmentioning

confidence: 99%

Learning, Generalization, and Scalability of Abstract Myoelectric Control

Dyson

Dupan

Jones

et al. 2020

IEEE Trans. Neural Syst. Rehabil. Eng.

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Motor learning-based methods offer an alternative paradigm to machine learning-based methods for controlling upper-limb prosthetics. Within this paradigm, the patterns of muscular activity used for control can differ from those which control biological limbs. Practice expedites the learning of these new, functional patterns of muscular activity. We envisage that these methods can result in enhanced control without increasing device complexity. However, key questions about training protocols, generalisation and scalability of motor learning-based methods have remained. In this work, we pursue three objectives: 1) to validate the motor learning-based abstract myoelectric control approach with people with upper-limb difference for the first time; 2) to test whether, after training, participants can generalize their learning to tasks of increased difficulty; and 3) to show that abstract myoelectric control scales with additional input signals, offering a larger control range. In three experiments, 25 limb-intact participants and 8 people with a limb difference (congenital and acquired) experienced a motor learning-based myoelectric controlled interface. We show that participants with upper-limb difference can learn to control the interface and that performance increases with experience. Across experiments, participant performance on easier lower target density tasks generalized to more difficult higher target density tasks. A proof-of-concept study demonstrates that learning-based control scales with additional myoelectric channels. Our results show that human motor learning-based approaches can enhance the number of distinct outputs from the musculature, thereby increasing the functionality of prosthetic hands and providing a viable alternative to machine learning.

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“…Serious games for prosthetic training can only be expected to yield fruitful results beyond engagement when they incorporate tasks that are found to facilitate prosthetic skill. We recommend that the research community investigates which types of myogame tasks might facilitate transfer, as the only existing results at the time of writing this paper indicate a lack of effectiveness [ 19 , 30 , 73 ]. This lack does not necessarily hold true for all tasks that are not related to activities of daily living, however, and ignoring abstract tasks entirely would exclude a wide range of possible avenues for prosthetic game development.…”

Section: Where Do We Go From Here?mentioning

confidence: 99%

“…More long-term and ideally home-based experiments are needed to conclusively test for any prosthetic skill transfer that might occur with the consistent use of prosthetic gaming devices. Even though previous studies indicate that no change in prosthetic ability occurs after training with a myogame [ 19 , 30 , 73 ], these only tested the effect of comparatively short training sessions with able-bodied people or very small groups of prosthesis users. It should also be tested whether prosthetic gaming has the potential to support traditional prosthetic training by allowing for supplementary practice sessions between visits to the prosthetist.…”

Section: Where Do We Go From Here?mentioning

confidence: 99%

Serious Games Are Not Serious Enough for Myoelectric Prosthetics

Garske¹,

Dyson²,

Dupan³

et al. 2021

JMIR Serious Games

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Serious games show a lot of potential for use in movement rehabilitation (eg, after a stroke, injury to the spinal cord, or limb loss). However, the nature of this research leads to diversity both in the background of the researchers and in the approaches of their investigation. Our close examination and categorization of virtual training software for upper limb prosthetic rehabilitation found that researchers typically followed one of two broad approaches: (1) focusing on the game design aspects to increase engagement and muscle training and (2) concentrating on an accurate representation of prosthetic training tasks, to induce task-specific skill transfer. Previous studies indicate muscle training alone does not lead to improved prosthetic control without a transfer-enabling task structure. However, the literature shows a recent surge in the number of game-based prosthetic training tools, which focus on engagement without heeding the importance of skill transfer. This influx appears to have been strongly influenced by the availability of both software and hardware, specifically the launch of a commercially available acquisition device and freely available high-profile game development engines. In this Viewpoint, we share our perspective on the current trends and progress of serious games for prosthetic training.

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Performance among different types of myocontrolled tasks is not related

Cited by 11 publications

References 45 publications

Addressing unpredictability may be the key to improving performance with current clinically prescribed myoelectric prostheses

Addressing unpredictability may be the key to improving performance with current clinically prescribed myoelectric prostheses

Learning, Generalization, and Scalability of Abstract Myoelectric Control

Serious Games Are Not Serious Enough for Myoelectric Prosthetics

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